![]() The expected suggestions could differ depending on user demography, previous search queries and current trends. Everyday, Billions of keystrokes across 100s of languages are served by Bing Autosuggest in less than 100 ms. Query Auto Completion (QAC) aims to help users reach their search intent faster and is a gateway to search for users. Manish Gupta (Microsoft,India) Puneet Agrawal (Microsoft,India) T6: Deep Learning Methods for Query Auto Completion In particular, the tutorial will focus on adversarial attacks and defense mechanisms in the context of agents based on multi-armed bandits, reinforcement learning, and multi-agent interactions. This tutorial will provide an overview of recent research on adversarial learning in sequential decision-making settings. PETERS ANOMALY SOFTWAREGoran Radanovic (Max Planck Institute for Software Systems) Adish Singla (MPI-SWS) Wen Sun (Cornell University) Xiaojin Zhu (University of Wisconsin-Madison) T4: Adversarial Sequential Decision-Making ![]() No prior background of social choice theory or the distortion framework will be necessary. Towards the end, the tutorial will present applications of the framework to other research areas. This will be followed by a survey of information-distortion tradeoff, where ranked ballots are replaced by more or less expressive ballot formats. Optimal distortion bounds for deterministic and randomized voting rules for aggregating ranked ballots will be covered under both utilitarian and metric cost settings. ![]() This tutorial will begin by surveying distortion in voting theory. Originally proposed in the context of voting, it has since been extended to fair division, matching, graph algorithms, and beyond. The distortion framework offers a way to quantitatively evaluate economic efficiency of collective decision-making algorithms. Nisarg Shah (University of Toronto), Dominik Peters (CNRS, LAMSADE, Universite Paris Dauphine) ![]() T3: Distortion in Social Choice & Beyond It is intended to introduce the background, several state-of-the-art algorithms and their theoretical guarantees, and some fundamental analytical techniques in this area. This tutorial focuses on pure exploration of the multi-armed bandit (MAB) problem. Zixin Zhong (National University of Singapore), Vincent Tan (National University of Singapore) T2: Pure Exploration in Multi-Armed Bandits Our tutorial aims to make these techniques easier to learn and use in real applications. The potential audience will be machine learning researchers and industry practitioners, with special interest in transfer learning, domain adaptation and generalization. ![]() The purpose of DG is to learn a generalized model from one or several training domains with different probability distributions that can achieve good out-of-distribution generalization. This tutorial is dedicated to introducing the latest advancements in Domain Generalization (DG).ĭifferent from transfer learning and domain adaptation that assume the availability of target domain data, DG takes a step further that does not require the access of target data. Jindong Wang (Microsoft Research) Haoliang Li (City University of Hong Hong) Sinno Pan (NTU, Singapore) Best Papers from Sister Conferences Accepted Papersįor up-to-date information on date, time, and location of the tutorials below, we refer to the IJCAI 2022 Schedule.Special Track on AI, the Arts and Creativity Accepted Papers.Special Track on AI for Good Accepted Papers.CALL FOR DIVERSITY AND INCLUSION ACTIVITIES.CALL FOR PAPERS: AI, THE ARTS AND CREATIVITY (SPECIAL TRACK).CALL FOR PAPERS: MULTI-YEAR TRACK ON AI FOR GOOD (SPECIAL TRACK). ![]()
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